February 17, 2023
Conference Paper

QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation


Quantum Machine Learning continues to be a highly active area of interest within Quantum Computing. Many of these approaches have adapted classical approaches to the quantum settings, such as QuantumFlow, etc. We push forward this trend, and demonstrate an adaption of the Classical Convolutional Neural Networks to quantum systems - namely QuCNN. QuCNN is a parameterised multi-quantum-state based neural network layer computing similarities between each quantum filter state and each quantum data state. With QuCNN, back propagation can be achieved through a single-ancilla qubit quantum routine. QuCNN is validated by applying a convolutional layer with a data state and a filter state over a small subset of MNIST images, comparing the backpropagated gradients, and training a filter state against an ideal target state.

Published: February 17, 2023


Stein S.A., Y. Mao, J.A. Ang, and A. Li. 2022. QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation. In Proceedings of the 7th ACM/IEEE Symposium on Edge Computing (SEC 2022), December 5-8, 2022, Seattle, WA, 368-374. Piscataway, New Jersey:IEEE. PNNL-SA-178064. doi:10.1109/SEC54971.2022.00054